class_docstr: Raw object from an Eximia EEG file.
  
  Parameters
  ----------
  fname : path-like
      Path to the eXimia data file (.nxe).
  %(preload)s
  %(verbose)s
  
  See Also
  --------
  mne.io.Raw : Documentation of attributes and methods.
code: def __init__(self, fname, preload=False, verbose=None):
      fname = str(_check_fname(fname, "read", True, "fname"))
      data_name = op.basename(fname)
      logger.info("Loading %s" % data_name)
      # Create vhdr and vmrk files so that we can use mne_brain_vision2fiff
      n_chan = 64
      sfreq = 1450.0
      # data are multiplexed int16
      ch_names = ["GateIn", "Trig1", "Trig2", "EOG"]
      ch_types = ["stim", "stim", "stim", "eog"]
      cals = [
          0.0015259021896696422,
          0.0015259021896696422,
          0.0015259021896696422,
          0.3814755474174106,
      ]
      ch_names += (
          "Fp1 Fpz Fp2 AF1 AFz AF2 "
          "F7 F3 F1 Fz F2 F4 F8 "
          "FT9 FT7 FC5 FC3 FC1 FCz FC2 FC4 FC6 FT8 FT10 "
          "T7 C5 C3 C1 Cz C2 C4 C6 T8 "
          "TP9 TP7 CP5 CP3 CP1 CPz CP2 CP4 CP6 TP8 TP10 "
          "P9 P7 P3 P1 Pz P2 P4 P8 "
          "P10 PO3 POz PO4 O1 Oz O2 Iz".split()
      )
      n_eeg = len(ch_names) - len(cals)
      cals += [0.07629510948348212] * n_eeg
      ch_types += ["eeg"] * n_eeg
      assert len(ch_names) == n_chan
      info = create_info(ch_names, sfreq, ch_types)
      n_bytes = _file_size(fname)
      n_samples, extra = divmod(n_bytes, (n_chan * 2))
      if extra != 0:
          warn(
              f"Incorrect number of samples in file ({n_samples}), the file is likely"
              " truncated"
          )
      for ch, cal in zip(info["chs"], cals):
          ch["cal"] = cal
      super().__init__(
          info,
          preload=preload,
          last_samps=(n_samples - 1,),
          filenames=[fname],
          orig_format="short",
      )
  
file_path: mne-python/mne/io/eximia/eximia.py
imports: import os.path
  from ..._fiff.meas_info import create_info
  from ..._fiff.utils import _file_size, _read_segments_file
  from ...utils import _check_fname, fill_doc, logger, verbose, warn
  from ..base import BaseRaw
label_desc: Use image node_id 1 to create a new RawEximia object from inherited base classes: BaseRaw with example: obj = RawEximia(fname, preload, verbose)